






'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_fnet import FNetTokenizer
else:
  __lowerCAmelCase        =							None

__lowerCAmelCase        =							logging.get_logger(__name__)
__lowerCAmelCase        =							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase        =							{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase        =							{
    "google/fnet-base": 5_12,
    "google/fnet-large": 5_12,
}

__lowerCAmelCase        =							"▁"




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : int      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : int      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : List[Any]      			=     ['''input_ids''', '''token_type_ids''']
  _a  : str      			=     FNetTokenizer







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=False   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="[SEP]"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="[CLS]"   ,	UpperCamelCase__="[MASK]"   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			(
             AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__   ,	normalized=UpperCamelCase__	)
             if isinstance(UpperCamelCase__   ,	UpperCamelCase__	)
             else mask_token
         )

         super().__init__(
             UpperCamelCase__   ,	tokenizer_file=UpperCamelCase__   ,	do_lower_case=UpperCamelCase__   ,	remove_space=UpperCamelCase__   ,	keep_accents=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         a_						     =			do_lower_case
         a_						     =			remove_space
         a_						     =			keep_accents
         a_						     =			vocab_file
         a_						     =			False if not self.vocab_file else True







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]
         if token_ids_a is None:
                return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase__	):
                copyfile(self.vocab_file   ,	UpperCamelCase__	)

         return (out_vocab_file,)












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




